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import base64
from PIL import Image
import io

# Assuming `response_json['image']` contains the base64 string
image_data = base64.b64decode(response_json['image'])
image = Image.open(io.BytesIO(image_data))
image.show()








# device2_server.py
from flask import Flask, request, jsonify
from PIL import Image
import io
import cv2
import numpy as np
import model_det2


app = Flask(__name__)

@app.route('/process-image', methods=['POST'])
def process_image():
    file = request.files['image']
    image = Image.open(file.stream).convert('RGB')  # Ensure it's RGB

    # Convert PIL image to OpenCV format
    image_np = np.array(image)
    
    
    cv2.imshow("raw", cv2.resize(image_np, None, fx=0.25, fy=0.25))
    cv2.waitKey(100)
    # Do your image processing here, return list of values
    #values = ["value1", "value2", "value3"]
    
    im1, obbs, classes, conf = model_det2.recognise_det2(image_np)
    
    cv2.imshow("ann", cv2.resize(im1, None, fx=0.25, fy=0.25))
    cv2.waitKey(100)
    
    #return jsonify(classes)
    
    # Convert im1 (OpenCV image) to base64 string
    _, buffer = cv2.imencode('.jpg', im1)
    im1_bytes = buffer.tobytes()
    im1_base64 = base64.b64encode(im1_bytes).decode('utf-8')

    return jsonify({
        'image': im1_base64,
        'obbs': obbs,
        'classes': classes,
        'conf': conf
    })


if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)  # Expose on local network